Titlebook: RoboCup 2019: Robot World Cup XXIII; Stephan Chalup,Tim Niemueller,Mary-Anne Williams Conference proceedings 2019 Springer Nature Switzerl

CHART

Yoshiaki Mizuchi,Tetsunari Inamuras important designs were dropped. This book is about devices commonly called sensors. The invention of a microprocessor has brought highly sophisticated instruments into our everyday life. Numerous 978-1-4939-0040-4978-1-4419-6466-3

镇痛剂

Delude

Occlusion

Collision Avoidance for Indoor Service Robots Through Multimodal Deep Reinforcement Learningt collision avoidance policy from scratch. Furthermore, we show empirically the generalization capabilities of the trained policy by testing it in challenging real-world environments. Videos showing the behavior of agents trained using the proposed method can be found at ..

Culmination

YoloSPoC: Recognition of Multiple Object Instances by Using Yolo-Based Proposals and Deep SPoC-Basedbjects. The obtained results show that the proposed method outperforms existing methods in the reported experiments, being robust against conditions like (i) occlusions, (ii) illumination changes, (iii) cluttered backgrounds, (iv) presence of multiple objects in the scene, (v) presence of textured a

吞下

Toward Data Driven Development in RoboCupcted during the competitions in 2018 and an algorithm for visual detection and tracking of robots in the RoboCup videos. A first empirical evaluation shows promising results and demonstrates how such data can be integrated and used to validate robot’s behavior.

somnambulism

明确

Estimation of Subjective Evaluation of HRI Performance Based on Objective Behaviors of Human and Rob and stored. Additionally, to acquire subjective evaluation results, we asked third-parties to evaluate the HRI performance by reviewing the stored interaction histories. From the analysis of the relationship between objective factors and subjective evaluation results, we demonstrate that the subjec

Feckless

南极

JET-Net: Real-Time Object Detection for Mobile Robotsresent a technique that reuses the learned features to obtain more information about the detected objects. Since the additional information can entirely be learned from simulation data, it is called ..